Opinion mining refers to a broad area of natural language processing, computational linguistics, and text mining. It aims at determining the attitude of a speaker or a writer with respect to some topic, written in natural language. The target corpora of Opinion Mining applications are social networks, blogs, e-forums (i.e. the blogosphere), that are a breeding ground of topics and opinions. Opinion mining has many applications related to business analytics, as companies, organizations and government agencies increasingly require new tools to detect customer's opinion about their products and/or services.
A difficult and related aspect of opinion mining involves problems associated with aspect-based sentiment analysis. The basic idea behind aspect based sentiment mining is the ability to determine sentiments or opinions that are expressed regarding different features or aspects of entities. When a text is classified at a document level or a sentence level, the resulting classification might not provide meaningful data concerning what the opinion holder likes or dislikes. If a document is positive on an object, for example, this clearly does not mean that the opinion holder will hold positive opinions about all the aspects or features of the object. Similarly, if a document is negative it does not mean that the opinion holder will dislike everything about the object described.
For example, consider the following sentence extracted from a restaurant review: “Pizza and garlic knots are great also and the delivery is super quick also.” Aspect based opinion mining should detect from the two food-related terms (the aspect terms: Pizza, garlic knot) that are positively commented that one aspect category of the sentence is food with positive polarity, and also, from the positively commented service-related term (the aspect term: delivery) that the other aspect category is service also with positive polarity. So basically the task involves detecting relevant aspect terms; detecting the polarity of these terms; detecting relevant aspect categories; and detecting the polarity of these categories.
Some systems have been implemented, which combine the use of a sentiment detection module based on deep syntactic parsing with machine learning classification components of a standard classification library. For example, a preliminary system aimed at performing aspect-based opinion mining has been developed. Such an opinion extraction system has been designed on top of a robust syntactic parser, which is used as a fundamental component of our system, in order to extract deep syntactic dependencies, which are an intermediary step of the extraction of semantic relations of opinion. Such a system uses a polar lexicon combined with syntactic dependencies extracted by the parser into opinion relation extraction rules.
Syntactic relations already extracted by a general dependency grammar, lexical information about word polarities, sub-categorization information, and syntactic dependencies are all combined within our robust parser to extract the semantic relations. The polarity lexicon has been built using existing resources and also by applying classification techniques over large corpora, while the semantic extraction rules are handcrafted for the complete description of these different components. The system outputs a semantic dependency called SENTIMENT which can be binary, i.e. linking opinionated terms and their targets, or unary, i.e. just the polar term in case the target of the opinion hasn't been detected. For example, when parsing “I was highly disappointed by their service and food.”, the systems outputs the following dependencies:
SUBJ-N(disappointed,food)
SUBJ-N(disappointed,service)
OBJ-N(disappointed,I)
MANNER-PRE(disappointed,highly)
SENTIMENT_NEGATIVE(disappointed,service)
SENTIMENT_NEGATIVE(disappointed,food)
In this system, aspects terms are not explicitly extracted; however, all non-polar arguments of the SENTIMENT dependency are potential aspect terms. Moreover, this system considers only positive and negative opinions, but does not cover the neutral and conflict polarities.